Planning Land-Use Change for Biomass-Fuelled Energy-Production Plants: Spatial Analyses Applied to the Case of Sardinia, Italy
Bibliographic record
Abstract
Land-use change in Sardinia is a delicate problem. On the one hand, the Regional Landscape Plan, the main landscape-planning tool adopted on the island, pays great attention to landscape protection, using strict constraints and directives for land management. On the other hand, the Regional Energy Plan aims at the diversification of energy sources and, in particular, of renewable energy sources (RES). Actions directed to the development of RES-based energy production may lead to conflicts between the two plans, especially when the associated land-use changes affect landscapes. The aim of this study is to present a decision-support method for the development of a biomass supply chain that does not compromise landscape values in Sardinia. Le changement quant à l'utilisation des terres en Sardaigne est un problème délicat. D'un côté, le Regional Landscape Plan, le principal outil de planification du territoire adopté sur l'ile, porte une attention particulière à la protection du territoire et aux moyens de contraintes, ainsi que des directives strictes pour la gestion des terres. D'un autre côté, le Regional Energy Plan favorise une diversification des sources d'énergie et, en particulier, des sources d'énergie renouvelable (SER). Des actions axées sur le développement d'une production énergétique basée sur les SER provoquent des conflits entre les deux plans, particulièrement quand les changements associés à l'utilisation des terres affectent le territoire. La présente étude vise à présenter une méthode pour soutenir les décisions lors du développement d'une chaine d'approvisionnement qui ne compromet pas les valeurs territoriales de la Sardaigne.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".